Gastos Casa
06 October, 2025
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 27/7/2025 | Electricidad | 52000 | Andrés | NA |
| 27/7/2025 | Comida | 59147 | Tami | Supermercado |
| 29/7/2025 | Comida | 10000 | Andrés | complemento comida |
| 29/7/2025 | Electrodomésticos/mantención casa | 68000 | Andrés | NA |
| 30/7/2025 | Comida | 24140 | Tami | Supermercado |
| 31/7/2025 | Gas | 19100 | Andrés | NA |
| 3/8/2025 | Comida | 86089 | Tami | Supermercado |
| 10/8/2025 | Comida | 108649 | Tami | Supermercado |
| 12/8/2025 | Enceres | 13490 | Tami | Confort |
| 16/8/2025 | VTR | 22000 | Andrés | NA |
| 17/8/2025 | Comida | 72586 | Tami | Supermercado |
| 20/8/2025 | Electricidad | 65242 | Andrés | 49393- 42306 -47872= 1521 |
| 25/8/2025 | Comida | 35500 | Andrés | ida al super |
| 25/8/2025 | Comida | 35000 | Andrés | piwen |
| 27/8/2025 | Comida | 37314 | Tami | Supermercado |
| 30/8/2025 | Comida | 67203 | Tami | Supermercado |
| 31/8/2025 | Diosi | 28000 | Andrés | american litter |
| 1/9/2025 | Comida | 7800 | Andrés | piwen |
| 7/9/2025 | Comida | 93538 | Tami | Supermercado |
| 10/9/2025 | Diosi | 49990 | Andrés | braloy 7.5 kg |
| 13/9/2025 | Comida | 60499 | Tami | Supermercado |
| 23/9/2025 | Comida | 76691 | Tami | Supermercado |
| 28/9/2025 | Comida | 87200 | Tami | Supermercado |
| 30/9/2025 | Gas | 82000 | Andrés | 79500+propina |
| 24/9/2025 | Electrodomésticos/mantención casa | 40000 | Andrés | mantención calefont 50 mil |
| 23/9/2025 | Gas | 80000 | Andrés | gas 79 mil + propina |
| 30/9/2025 | VTR | 22000 | Andrés | NA |
| 4/10/2025 | Comida | 67440 | Tami | Supermercado |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
theme_custom() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
# Assuming your time series starts on "2019-03-03"
start_date <- as.Date("2019-03-03")
frequency <- 7 # Weekly data
num_periods <- length(tsData_gastos$x) # Total number of periods in your time series
# Generate sequence of dates
dates <- tsData$day# seq.Date(from = start_date, by = "day", length.out = num_periods)
# Create a data frame from the decomposed time series object
tsData_gastos_df <- data.frame(
day = dates,
Actual = as.numeric(tsData_gastos$x),
Seasonal = as.numeric(tsData_gastos$seasonal),
Trend = as.numeric(tsData_gastos$trend),
Random = as.numeric(tsData_gastos$random)
)
tsData_gastos_long <- tsData_gastos_df %>%
pivot_longer(cols = c("Actual", "Seasonal", "Trend", "Random"),
names_to = "Component", values_to = "Value")
# Plotting with facet_wrap
ggplot(tsData_gastos_long, aes(x = day, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición de los Gastos Diarios", x = "Date", y = "Value") +
scale_x_date(date_breaks = "3 months", date_labels = "%m %Y") +
facet_wrap(~ Component, scales = "free_y", ncol=1) +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(strip.text = element_text(size = 12))
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 1.0429e+09 2 5.4216 0.0046 **
## lag_depvar 2.6496e+11 1 2754.8426 <2e-16 ***
## Residuals 8.2907e+10 862
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 -1728.424 16186.10 0.1407873
## 2-0 31456.791 23404.047 39509.53 0.0000000
## 2-1 24227.953 19575.584 28880.32 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 848 51313.14 2 51727.43
## 849 56125.29 2 51313.14
## 850 68503.71 2 56125.29
## 851 62945.00 2 68503.71
## 852 50209.71 2 62945.00
## 853 49436.29 2 50209.71
## 854 55308.00 2 49436.29
## 855 62650.86 2 55308.00
## 856 55683.00 2 62650.86
## 857 49762.14 2 55683.00
## 858 51835.00 2 49762.14
## 859 49806.43 2 51835.00
## 860 52799.57 2 49806.43
## 861 50620.71 2 52799.57
## 862 49192.00 2 50620.71
## 863 66245.86 2 49192.00
## 864 62359.71 2 66245.86
## 865 70816.86 2 62359.71
## 866 84559.71 2 70816.86
## 867 80831.43 2 84559.71
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 710 53691.05 21783.263
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
## [610] 54939.71 51169.00 43113.29 56289.71 60739.86 50363.14 62270.86
## [617] 67061.57 59609.00 85054.00 68023.29 59242.29 61535.14 56215.86
## [624] 45152.29 57409.57 35151.43 34991.43 45944.71 57944.71 55706.29
## [631] 88593.71 77359.43 79878.71 81753.00 75716.00 67381.43 63528.57
## [638] 49682.86 47815.00 46546.14 44808.71 42959.57 46023.86 51309.57
## [645] 68447.29 84959.29 81666.29 82700.86 89422.14 104812.71 98812.71
## [652] 64779.86 61862.86 58376.43 59503.57 55429.43 44454.57 47184.00
## [659] 52126.71 51202.00 64437.14 64297.14 64628.57 51413.14 52969.43
## [666] 54135.29 48799.43 41907.86 45382.00 42633.29 46624.71 44051.86
## [673] 35852.86 29737.71 29734.86 32881.71 38298.57 40886.14 38601.86
## [680] 38628.86 39142.57 32666.14 39911.57 39336.29 39678.86 41963.14
## [687] 54220.57 63901.86 73116.00 60863.86 56293.86 52725.00 58625.00
## [694] 47513.00 40300.14 33312.43 29556.71 27816.71 34120.29 32132.57
## [701] 32902.57 39694.14 72501.29 79551.14 99637.71 95424.29 98395.14
## [708] 115594.71 114267.57 88353.29 88750.86 78835.71 75519.14 73202.86
## [715] 53433.29 48165.71 52163.14 49306.86 36846.86 43220.57 38952.29
## [722] 41522.29 39090.00 28452.57 32975.00 33690.71 26405.29 47087.43
## [729] 49660.29 47409.71 53881.71 45189.57 45503.86 54640.14 39131.29
## [736] 35024.14 44755.43 41063.29 42783.29 45952.57 44937.43 40838.43
## [743] 48838.43 43139.14 67134.29 73224.29 68770.71 59539.29 82179.86
## [750] 74252.14 73015.00 56116.43 111885.00 131425.14 136678.00 115531.29
## [757] 118310.86 117449.43 115193.57 61025.43 43913.86 46099.29 44524.86
## [764] 42208.71 166486.57 171565.29 200415.71 204498.14 197558.86 195266.57
## [771] 203144.29 85493.71 74721.57 36232.14 40161.71 40629.86 45663.71
## [778] 39252.29 39618.57 39438.43 44650.71 38626.71 38280.43 44134.14
## [785] 47596.43 45598.43 42564.29 45699.14 49553.86 50018.43 43772.86
## [792] 39235.43 39905.00 40374.43 34230.57 34324.14 33491.57 33366.43
## [799] 46646.86 49770.86 57339.86 59799.14 53577.14 61775.29 70627.86
## [806] 57888.43 49960.71 42923.71 47284.86 52284.86 50191.00 36465.86
## [813] 34525.14 43199.14 52757.43 43200.86 36772.29 29568.00 42362.00
## [820] 42566.29 39596.00 32925.00 43416.57 52624.86 57733.71 54120.57
## [827] 53353.43 56286.86 60626.86 61375.29 53710.86 55795.57 55130.14
## [834] 57700.14 61333.14 59230.71 49195.00 55436.43 50353.14 43194.86
## [841] 47539.71 35271.00 34774.86 48788.71 50717.71 51727.43 51313.14
## [848] 56125.29 68503.71 62945.00 50209.71 49436.29 55308.00 62650.86
## [855] 55683.00 49762.14 51835.00 49806.43 52799.57 50620.71 49192.00
## [862] 66245.86 62359.71 70816.86 84559.71 80831.43
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [741] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [778] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [815] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [852] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 2017.80530 4039.90880 -537.57717 2438.50043 -2968.65351 519.10161
## 8 9 10 11 12 13
## -5655.32556 -1188.28784 -3966.66454 -419.01928 -4940.60487 -1610.93182
## 14 15 16 17 18 19
## -901.24804 376.14026 -3243.83663 -379.17416 -2131.14240 6602.99077
## 20 21 22 23 24 25
## -1529.09831 -1208.39875 1475.39366 -1186.47211 234.64343 1695.13860
## 26 27 28 29 30 31
## -7101.52529 947.00628 8192.30582 419.79375 -12.29847 -2398.87763
## 32 33 34 35 36 37
## 1577.50052 4574.13666 1129.37450 2394.03920 -1865.06457 4610.42136
## 38 39 40 41 42 43
## 4305.22830 -2273.34196 -2979.71531 -1109.11663 -10740.71732 7288.17369
## 44 45 46 47 48 49
## 2557.45231 1367.45857 8105.94701 688.24197 6530.73501 6717.27112
## 50 51 52 53 54 55
## -5878.63424 -4793.15983 -5058.50892 -7928.32053 6128.71548 -4076.52472
## 56 57 58 59 60 61
## -4894.93160 3854.45191 887.28643 -32.45215 141.92125 -4996.67625
## 62 63 64 65 66 67
## 18125.72895 3642.80976 -3643.57140 5927.04064 7346.60921 14642.50707
## 68 69 70 71 72 73
## 1699.02044 -13207.16861 -1303.28765 4645.94117 -4897.27107 -4402.55772
## 74 75 76 77 78 79
## -10496.26172 2466.54702 -5399.40815 1063.48981 -6866.17162 546.20131
## 80 81 82 83 84 85
## -2354.95876 -2694.36719 -3932.51979 -538.44765 2313.87677 3762.42339
## 86 87 88 89 90 91
## 477.18264 -484.26341 196.77397 4302.09051 -1162.32067 1151.30285
## 92 93 94 95 96 97
## -2063.91471 -1044.06358 177.66656 274.89650 -7483.88743 2391.04714
## 98 99 100 101 102 103
## -8602.72298 -2941.58975 -4040.41006 -1737.70159 -1262.07407 3180.44044
## 104 105 106 107 108 109
## -2342.00379 2593.90931 -1158.13573 970.63900 2587.41716 -3153.77433
## 110 111 112 113 114 115
## -4722.80709 -850.47295 1903.35026 11693.68856 -1241.12907 2669.71795
## 116 117 118 119 120 121
## 4264.25563 3504.49618 -1097.84233 -4714.50078 -3722.90014 2320.53111
## 122 123 124 125 126 127
## -1731.59946 1341.27087 8859.25476 849.10112 132.40492 -2519.42917
## 128 129 130 131 132 133
## 2656.49838 7054.18392 1014.80397 -8497.12358 1750.30647 4136.75431
## 134 135 136 137 138 139
## -3162.32557 -1418.59416 -853.06309 -3879.21918 1183.39219 -494.95373
## 140 141 142 143 144 145
## -2913.12018 1718.34319 -1880.66057 -7829.07739 2038.85847 -3479.95990
## 146 147 148 149 150 151
## 2101.66017 -257.73820 1022.76696 -359.42256 1352.04656 1186.64897
## 152 153 154 155 156 157
## 3356.72207 -4861.29030 -1174.53882 -3235.96410 5956.26731 9746.91620
## 158 159 160 161 162 163
## -3699.65855 -5046.08790 3336.19777 -72.35489 2429.16541 -6177.50437
## 164 165 166 167 168 169
## -7014.79496 3889.10528 17124.67698 3352.50312 -675.36239 -2722.95185
## 170 171 172 173 174 175
## -1380.63451 3314.05736 -506.03791 -8353.42807 2587.67086 4048.06028
## 176 177 178 179 180 181
## 346.34409 8470.71972 -9531.38930 -3751.64951 -11023.71198 -11516.24549
## 182 183 184 185 186 187
## 961.58318 9018.55170 -1709.03085 5649.31515 6272.15903 12869.95958
## 188 189 190 191 192 193
## 8132.91258 -4368.53256 2159.07770 10059.18883 -1961.38285 -2762.37599
## 194 195 196 197 198 199
## -10597.43849 -6673.65487 927.45778 -5539.89123 -10098.56036 5088.28248
## 200 201 202 203 204 205
## -3366.48820 -2008.07155 -1098.41733 6200.45979 9581.22430 266.78746
## 206 207 208 209 210 211
## 2611.66130 2782.04807 5465.88505 12510.75943 -6020.06279 -11622.08087
## 212 213 214 215 216 217
## -5980.94839 -10895.73692 -5373.41790 1233.56147 -13304.66814 16106.51035
## 218 219 220 221 222 223
## 7510.96686 1221.60958 26379.72819 12190.91266 6988.03058 13674.53076
## 224 225 226 227 228 229
## -4276.56416 -2096.38460 3427.43058 10.69731 2401.32292 8662.51354
## 230 231 232 233 234 235
## 5486.57232 -2247.74159 -2162.30759 9097.25511 -11841.72401 -7603.72123
## 236 237 238 239 240 241
## -8853.43365 -10405.41751 2782.71580 1054.52961 -8595.63915 -9280.59072
## 242 243 244 245 246 247
## 8811.26478 -8058.29976 2200.86969 -10591.07521 -4335.33995 1143.25757
## 248 249 250 251 252 253
## 720.05342 -12601.93161 3367.26252 1779.97721 3924.61691 1840.86126
## 254 255 256 257 258 259
## -1458.67475 10840.52959 20567.63557 2856.37632 -4616.07924 3770.43806
## 260 261 262 263 264 265
## -2037.73550 3397.40806 -5194.64039 -11229.06074 -5049.82025 -836.93067
## 266 267 268 269 270 271
## -5503.08133 8468.99855 -4602.60148 3871.67397 -2431.61559 4108.06366
## 272 273 274 275 276 277
## 379.01237 6971.24142 -1755.13931 11683.79970 -4944.89196 1371.56295
## 278 279 280 281 282 283
## -728.35464 7496.79781 -5423.70839 -3086.77664 -11609.88713 -2996.07006
## 284 285 286 287 288 289
## 18333.54665 7429.40846 2367.28001 -998.37462 539.66850 6032.42667
## 290 291 292 293 294 295
## 6507.06166 -19157.42136 -11481.81090 -8438.22409 9366.69925 2754.90732
## 296 297 298 299 300 301
## -1501.64819 27082.66765 9688.88801 4506.95301 9119.20294 2443.96805
## 302 303 304 305 306 307
## -1443.35491 7495.64845 -24705.65936 -3881.00282 -508.17388 -7296.47491
## 308 309 310 311 312 313
## -4280.20381 2635.59897 -9493.96865 -3507.98021 -8455.46554 1316.05009
## 314 315 316 317 318 319
## -3406.17472 1798.59919 -4340.69820 27193.58669 -1067.70428 2950.00890
## 320 321 322 323 324 325
## 10481.90481 5215.49061 31997.53037 4655.05103 -21390.37872 1406.77964
## 326 327 328 329 330 331
## 728.67562 -6841.55245 -2086.39196 -33609.34482 667.93296 -2517.02636
## 332 333 334 335 336 337
## -300.90380 -3375.75069 3885.22637 -651.35072 -7167.40051 -3313.55389
## 338 339 340 341 342 343
## -2382.85545 -7868.11217 3680.91278 -1559.54900 -1927.19827 -1183.11477
## 344 345 346 347 348 349
## -14.62450 284.88956 -1821.66736 -9649.86072 -13390.85609 2161.05306
## 350 351 352 353 354 355
## -4488.29937 -3818.41595 -6136.97636 1603.09664 1221.19351 2576.03061
## 356 357 358 359 360 361
## -3961.42708 -707.42514 480.08740 6807.00447 40.86611 -278.95213
## 362 363 364 365 366 367
## 2339.06419 -3005.35384 -1124.23009 -8988.11557 -4841.13093 -6413.88292
## 368 369 370 371 372 373
## -5133.09086 -7424.27490 4861.42796 190.34314 6930.01834 -7857.79640
## 374 375 376 377 378 379
## -2461.30730 -3582.81386 -2654.73670 -12641.72218 1755.81448 -10797.19172
## 380 381 382 383 384 385
## 5561.41952 9169.58690 2916.38434 -2626.22206 1380.69194 6509.24635
## 386 387 388 389 390 391
## 11146.63381 -6111.83820 -5654.57720 -432.43370 8286.76712 1505.08753
## 392 393 394 395 396 397
## 10905.19283 -10236.37746 2453.52012 382.79545 231.90448 -984.01142
## 398 399 400 401 402 403
## -888.36691 -14808.00746 8264.23198 -1467.19475 -1650.87042 6711.08127
## 404 405 406 407 408 409
## -8228.47417 -1558.21497 -2784.81811 -6059.77073 -3074.81651 -4121.74637
## 410 411 412 413 414 415
## -8946.12832 5973.34769 1449.13908 -7576.72802 -7870.93965 14066.41810
## 416 417 418 419 420 421
## 3593.49556 4246.14692 -8304.98589 -4981.76516 -2820.11120 2609.87004
## 422 423 424 425 426 427
## -14234.82977 -2961.80001 -9263.38416 2878.03481 6820.44270 6381.29035
## 428 429 430 431 432 433
## -4214.99410 -4335.55293 -4923.69330 -1976.29861 -5896.60893 -6794.47358
## 434 435 436 437 438 439
## -6098.56089 -1527.79290 -987.03945 -5121.05525 2445.42045 4682.33376
## 440 441 442 443 444 445
## -5242.22903 -2330.53963 1405.23431 -4021.76280 2659.71391 -6771.84649
## 446 447 448 449 450 451
## -12283.98333 -4645.39694 9518.64758 -2205.03892 4582.91267 -6065.08749
## 452 453 454 455 456 457
## -1300.08780 205.32876 2841.07732 -12469.71084 3209.42680 -6880.23869
## 458 459 460 461 462 463
## 6362.40643 2821.58236 2300.85455 -4064.22047 1886.59544 -223.93434
## 464 465 466 467 468 469
## 1574.49251 -748.03369 3124.46383 -2878.44853 5576.37129 -7191.81322
## 470 471 472 473 474 475
## -3185.40827 -2414.43297 -4864.72916 2811.58990 7598.71570 -6246.10595
## 476 477 478 479 480 481
## 1277.90407 -6391.38417 -3034.90356 1829.56142 -13122.51692 -9905.33626
## 482 483 484 485 486 487
## -1325.60456 -108.16188 -1099.65860 -1484.21269 -9730.26605 10974.78076
## 488 489 490 491 492 493
## 6066.61957 7224.38072 -5661.54982 5160.88634 9065.87513 5793.96931
## 494 495 496 497 498 499
## -13751.91137 -10791.92478 -3628.91196 -1283.49508 -701.04672 -7803.93317
## 500 501 502 503 504 505
## 456.71994 4126.45584 5328.18197 457.87028 -125.37037 -7445.95478
## 506 507 508 509 510 511
## 387.36219 -5235.30941 1660.64135 -1478.07092 -8337.83949 -757.51964
## 512 513 514 515 516 517
## -2833.11125 -742.38240 1174.05928 -9663.05801 -7905.92466 24164.34424
## 518 519 520 521 522 523
## 9612.28601 5631.86591 -5601.35928 2554.27773 16767.24824 11165.82842
## 524 525 526 527 528 529
## -24488.93645 -5307.04424 -3960.38839 4358.91053 -586.04869 -11330.00236
## 530 531 532 533 534 535
## 4202.86646 13703.66442 -5231.67683 4138.06322 5304.91990 -2057.75025
## 536 537 538 539 540 541
## -4798.50402 -7312.60743 -2312.36361 8118.28396 -106.04249 -8373.55916
## 542 543 544 545 546 547
## 1613.36907 -809.10406 158.54704 -11240.27092 -11233.81208 1902.38849
## 548 549 550 551 552 553
## 6851.48072 -1499.04125 656.88680 -7906.73680 8402.39859 710.34415
## 554 555 556 557 558 559
## -12145.74693 8999.23062 8457.66339 -133.56017 4620.18510 -3823.74789
## 560 561 562 563 564 565
## 13872.93038 21215.00885 -6820.19237 -10001.55132 6495.94488 -71.20415
## 566 567 568 569 570 571
## 3163.16525 -7674.11105 -17571.46911 6465.58648 6215.70855 1663.55284
## 572 573 574 575 576 577
## 2856.57432 1521.39719 -2415.33015 14478.31245 -9935.47037 -6492.72545
## 578 579 580 581 582 583
## 8487.37329 2606.22263 -6803.73909 7277.13194 -4054.76821 -3013.42967
## 584 585 586 587 588 589
## 15477.09032 -14776.19253 8205.16203 -179.33831 -6458.14409 -974.72504
## 590 591 592 593 594 595
## 36.23428 -10867.99515 1619.43906 -7329.30063 2905.57987 8690.92104
## 596 597 598 599 600 601
## -7707.97187 5669.05803 2531.08316 6642.60872 -3425.40062 5927.66679
## 602 603 604 605 606 607
## -8538.73073 2046.15935 1054.56434 2920.38778 1268.98761 169.13309
## 608 609 610 611 612 613
## -6036.98329 7870.40329 -1413.10967 -2795.89009 -3663.32694 -8424.59955
## 614 615 616 617 618 619
## 11790.03951 4728.04644 -9536.72060 11437.04255 5824.08602 -5814.09259
## 620 621 622 623 624 625
## 26142.15701 -13119.64190 -7021.04864 2943.69508 -4378.84082 -10794.99662
## 626 627 628 629 630 631
## 11128.43933 -21838.79199 -2552.03791 8541.03847 10971.24395 -1751.48471
## 632 633 634 635 636 637
## 33091.64027 -6876.11794 5458.46965 5131.67642 -2542.87140 -5602.96620
## 638 639 640 641 642 643
## -2173.97769 -12653.48277 -2424.45463 -2061.38054 -2690.21919 -3021.38517
## 644 645 646 647 648 649
## 1658.48126 4266.95463 16786.58436 18325.50609 606.10918 4517.74729
## 650 651 652 653 654 655
## 10335.13656 19853.37663 406.76252 -28383.94458 -1566.72068 -2504.59064
## 656 657 658 659 660 661
## 1668.61583 -3390.30235 -10805.61481 1512.45507 4070.49034 -1172.63227
## 662 663 664 665 666 667
## 12870.42576 1166.99165 1620.73706 -11884.25789 1218.23772 1024.38099
## 668 669 670 671 672 673
## -5330.07583 -7559.75330 1935.49813 -3848.54584 2544.41151 -3515.72354
## 674 675 676 677 678 679
## -9466.83967 -8418.58450 -3078.69225 70.66116 2738.13542 593.04386
## 680 681 682 683 684 685
## -3951.98146 -1929.22005 -1439.09544 -8364.35191 4539.47838 -2366.07795
## 686 687 688 689 690 691
## -1520.88418 464.09973 10725.76689 9697.83933 10453.52347 -9848.93928
## 692 693 694 695 696 697
## -3714.34409 -3290.43028 5727.65049 -10539.13038 -8043.52565 -8729.42671
## 698 699 700 701 702 703
## -6380.03321 -4838.69691 2985.09803 -4509.99413 -2003.34471 4115.48413
## 704 705 706 707 708 709
## 30988.88758 9375.41719 23302.58713 1539.68836 8191.78296 22795.74124
## 710 711 712 713 714 715
## 6441.47604 -18313.29601 4725.37107 -5537.12664 -190.92031 390.45482
## 716 717 718 719 720 721
## -17355.39707 -5350.45856 3249.20331 -3099.60246 -13064.08942 4195.82312
## 722 723 724 725 726 727
## -5641.12370 658.04199 -4019.63132 -12531.99212 1284.26922 -1951.22468
## 728 729 730 731 732 733
## -9861.96686 17185.39428 1688.43542 -2810.01987 5628.28564 -8718.38972
## 734 735 736 737 738 739
## -809.85117 8051.84573 -15439.30817 -5996.49167 7323.17056 -4871.11560
## 740 741 742 743 744 745
## 74.67887 1741.21491 -2042.90649 -5254.98463 6326.27754 -6362.54156
## 746 747 748 749 750 751
## 22612.01977 7737.66326 -2036.69046 -7377.07076 23328.92294 -4379.67008
## 752 753 754 755 756 757
## 1309.56502 -14508.12499 56024.58758 26840.19395 15020.99099 -10715.10085
## 758 759 760 761 762 763
## 10540.17877 7250.26179 5747.02761 -46450.19164 -16235.50770 900.15835
## 764 765 766 767 768 769
## -2583.66097 -3524.23869 122777.21318 19275.39850 43688.59669 22564.64605
## 770 771 772 773 774 775
## 12058.57648 15829.08692 25709.55214 -98823.71266 -6805.53090 -35883.42795
## 776 777 778 779 780 781
## 1674.03673 -1291.05425 3333.79038 -7475.67727 -1507.77979 -2007.94343
## 782 783 784 785 786 787
## 3361.73160 -7216.19903 -2299.36610 3856.89513 2204.83941 -2818.13079
## 788 789 790 791 792 793
## -4106.63768 1679.12481 2794.94051 -108.31983 -6759.78345 -5840.50827
## 794 795 796 797 798 799
## -1206.62328 -1322.19369 -7876.18667 -2414.77841 -3329.10242 -2726.83455
## 800 801 802 803 804 805
## 10662.93030 2183.93043 7023.51765 2869.83109 -5500.82636 8133.42608
## 806 807 808 809 810 811
## 9823.34836 -10650.49815 -7447.87977 -7558.50181 2950.80935 4140.51515
## 812 813 814 815 816 817
## -2321.80036 -14217.55767 -4166.72893 6202.85699 8182.74113 -9724.82494
## 818 819 820 821 822 823
## -7803.89949 -9391.59587 9696.72857 -1276.99699 -4425.76543 -8501.65153
## 824 825 826 827 828 829
## 7818.31705 7860.20417 4923.85875 -3152.85005 -763.22008 2840.45482
## 830 831 832 833 834 835
## 4617.54270 1574.14941 -6744.17497 2036.88674 -449.93934 2701.44006
## 836 837 838 839 840 841
## 4089.05246 -1187.49796 -9386.33791 5623.21067 -4913.15922 -7630.22053
## 842 843 844 845 846 847
## 2968.77123 -13096.00856 -2873.07790 11574.25512 1259.46483 583.82787
## 848 849 850 851 852 853
## -712.63680 4461.46403 12635.56346 -3738.08080 -11616.76413 -1263.47961
## 854 855 856 857 858 859
## 5283.97278 7496.76204 -5886.48825 -5719.58662 1526.27410 -2313.33535
## 860 861 862 863 864 865
## 2452.15347 -2341.78766 -1866.85261 16435.26031 -2350.69553 9501.73798
## 866 867
## 15855.65983 120.35427
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17251.48 20099.09 24353.72 24071.64 26425.37 23757.61 24474.04 19705.43
## 10 11 12 13 14 15 16 17
## 19441.95 16784.30 17561.89 14290.79 14341.96 15006.72 16703.55 15023.32
## 18 19 20 21 22 23 24 25
## 16058.14 15431.58 22515.10 21598.97 21078.75 22969.04 22294.93 22947.58
## 26 27 28 29 30 31 32 33
## 24793.81 18721.28 20447.69 28286.21 28343.87 28016.73 25645.79 27048.43
## 34 35 36 37 38 39 40 41
## 30892.05 31240.53 32649.92 30160.15 34137.77 37346.34 34402.00 31212.40
## 42 43 44 45 46 47 48 49
## 30060.00 20638.11 28157.98 30594.83 31684.20 38523.33 38017.84 42680.73
## 50 51 52 53 54 55 56 57
## 46917.63 39614.45 34182.08 29204.03 22347.43 28638.38 25218.50 21515.55
## 58 59 60 61 62 63 64 65
## 25924.57 27184.31 27481.36 27893.25 23763.56 40357.33 42201.57 37446.82
## 66 67 68 69 70 71 72 73
## 41654.39 46570.78 57240.55 55254.03 40495.00 38000.49 41018.84 35318.13
## 74 75 76 77 78 79 80 81
## 30769.69 21471.74 24673.69 20598.80 22685.17 17579.94 19595.67 18822.08
## 82 83 84 85 86 87 88 89
## 17849.66 15918.30 17196.27 20804.86 25223.25 26213.26 26238.23 26855.05
## 90 91 92 93 94 95 96 97
## 30980.75 29811.13 30810.63 28874.78 28074.48 28442.67 28849.32 22425.81
## 98 99 100 101 102 103 104 105
## 25441.29 18470.73 17326.70 15367.13 15666.93 16344.42 20817.72 19901.09
## 106 107 108 109 110 111 112 113
## 23412.71 23202.65 24879.01 27756.20 25253.95 21696.90 21972.36 24619.03
## 114 115 116 117 118 119 120 121
## 35485.13 33677.71 35515.46 38514.22 40470.41 38158.50 32978.76 29319.61
## 122 123 124 125 126 127 128 129
## 31402.74 29682.44 30864.17 38465.04 38107.45 37168.86 34031.93 35813.39
## 130 131 132 133 134 135 136 137
## 41212.05 40652.27 31852.69 33117.67 36307.90 32718.02 31105.06 30189.93
## 138 139 140 141 142 143 144 145
## 26746.46 28161.10 27930.69 25616.66 27641.37 26265.93 19867.14 22898.10
## 146 147 148 149 150 151 152 153
## 20724.48 23702.02 24242.09 25832.71 26014.81 27669.21 28970.14 32002.72
## 154 155 156 157 158 159 160 161
## 27472.25 26735.11 24290.02 30184.94 41720.09 40050.09 37414.66 42435.64
## 162 163 164 165 166 167 168 169
## 43844.41 47260.79 42726.08 38032.61 43458.61 59763.07 61975.51 60389.38
## 170 171 172 173 174 175 176 177
## 57214.63 55613.66 58316.61 57340.57 49631.61 52455.51 56198.66 56234.85
## 178 179 180 181 182 183 184 185
## 63364.68 53865.65 50616.14 41423.53 32961.70 36470.45 46575.32 46031.26
## 186 187 188 189 190 191 192 193
## 51984.84 57730.61 68515.09 73798.68 67492.49 67685.95 74757.24 70433.09
## 194 195 196 197 198 199 200 201
## 65955.30 55197.65 49226.97 50651.46 46245.56 38413.29 44838.92 43066.07
## 202 203 204 205 206 207 208 209
## 42703.99 43182.40 49977.35 58867.78 58497.34 60222.38 61878.40 65670.10
## 210 211 212 213 214 215 216 217
## 75137.92 67219.65 55407.09 50015.17 41010.28 37967.58 41081.67 31100.49
## 218 219 220 221 222 223 224 225
## 48076.32 55398.10 56300.13 79068.66 86564.68 88568.18 96160.56 87110.24
## 226 227 228 229 230 231 232 233
## 81107.86 80689.73 77339.25 76500.63 81238.28 82602.74 77037.45 72249.74
## 234 235 236 237 238 239 240 241
## 77904.15 64550.15 56585.58 48535.13 40145.57 44338.04 46491.07 39940.88
## 242 243 244 245 246 247 248 249
## 33619.59 43903.44 38149.56 42085.79 34348.63 33054.31 36710.09 39534.36
## 250 251 252 253 254 255 256 257
## 30362.59 36301.45 40103.38 45298.85 48017.53 47510.04 57812.36 75311.91
## 258 259 260 261 262 263 264 265
## 75126.94 68436.70 69918.74 66139.02 67585.35 61342.20 50615.39 46642.22
## 266 267 268 269 270 271 272 273
## 46851.65 42957.86 51763.17 48035.75 52183.04 50299.36 54367.27 54663.33
## 274 275 276 277 278 279 280 281
## 60681.57 58315.49 67989.75 61913.72 62123.78 60472.63 66216.28 59945.92
## 282 283 284 285 286 287 288 289
## 56509.32 46060.21 44456.74 61691.31 67222.15 67631.66 65048.90 64136.14
## 290 291 292 293 294 295 296 297
## 68137.65 72048.42 53042.38 43143.08 37153.30 47476.09 50718.36 49832.19
## 298 299 300 301 302 303 304 305
## 74031.83 79978.05 80645.80 85258.89 83457.21 78486.78 81954.09 56849.43
## 306 307 308 309 310 311 312 313
## 53110.03 52789.76 46579.06 43788.12 47391.97 39943.12 38665.04 33225.81
## 314 315 316 317 318 319 320 321
## 37010.89 36192.12 40024.13 38008.27 63798.28 61639.13 63262.95 71262.22
## 322 323 324 325 326 327 328 329
## 73649.90 99135.23 97512.66 73339.36 72137.04 70494.12 62444.68 59566.49
## 330 331 332 333 334 335 336 337
## 29510.50 33198.60 33638.19 35958.46 35299.20 41067.07 42142.83 37389.70
## 338 339 340 341 342 343 344 345
## 36604.00 36730.68 32048.94 38048.83 38712.34 38970.83 39846.77 41632.97
## 346 347 348 349 350 351 352 353
## 43455.24 43206.86 36150.43 26716.80 32062.30 30923.13 30513.12 28129.19
## 354 355 356 357 358 359 360 361
## 32808.81 36563.68 41028.00 39216.71 40477.20 42616.00 50012.42 50563.09
## 362 363 364 365 366 367 368 369
## 50764.79 53228.35 50711.37 50155.83 42799.85 39996.17 36172.52 33950.85
## 370 371 372 373 374 375 376 377
## 30008.00 37297.09 39584.41 47471.22 41441.88 40888.96 39426.02 38958.72
## 378 379 380 381 382 383 384 385
## 29824.90 34423.76 27474.29 35694.98 46029.76 49595.79 47868.88 49860.90
## 386 387 388 389 390 391 392 393
## 56082.08 65569.12 58779.29 53246.58 52975.23 60356.06 60879.52 69549.66
## 394 395 396 397 398 399 400 401
## 58653.48 60220.63 59780.67 59264.44 57751.08 56512.44 43268.77 51855.91
## 402 403 404 405 406 407 408 409
## 50856.16 49822.20 56224.62 48765.79 48076.82 46403.20 42079.67 40910.17
## 410 411 412 413 414 415 416 417
## 38973.70 33066.80 40941.00 43867.87 38539.23 33626.58 48500.93 52346.42
## 418 419 420 421 422 423 424 425
## 56276.41 48744.19 45066.83 43742.56 47329.69 35746.66 35475.81 29733.54
## 426 427 428 429 430 431 432 433
## 35324.41 43653.57 50546.99 47311.84 44379.98 41304.58 41192.75 37669.90
## 434 435 436 437 438 439 440 441
## 33807.56 31041.08 32617.47 34467.20 32471.44 37338.52 43545.23 40296.97
## 442 443 444 445 446 447 448 449
## 40002.91 43009.91 40895.57 44885.85 40131.84 31162.40 29999.64 41358.75
## 450 451 452 453 454 455 456 457
## 41040.23 46692.52 42327.80 42677.53 44298.35 48017.28 37889.57 42739.81
## 458 459 460 461 462 463 464 465
## 38162.17 45732.70 49253.43 51874.51 48603.40 50944.65 51146.22 52893.61
## 466 467 468 469 470 471 472 473
## 52391.11 55335.45 52663.20 57715.38 50973.98 48584.43 47170.30 43793.98
## 474 475 476 477 478 479 480 481
## 47550.86 55015.68 49441.52 51145.10 45932.90 44311.58 47145.09 36557.19
## 482 483 484 485 486 487 488 489
## 30117.46 31987.16 34684.37 36174.64 37140.69 30780.22 43312.95 49974.48
## 490 491 492 493 494 495 496 497
## 56806.12 51516.54 56350.55 63985.74 67797.91 54051.50 44627.48 42652.07
## 498 499 500 501 502 503 504 505
## 42975.33 43766.65 38252.28 40651.69 45954.25 51636.99 52346.80 52457.38
## 506 507 508 509 510 511 512 513
## 46158.07 47498.31 43756.79 46512.79 46178.41 39892.95 41024.25 40199.24
## 514 515 516 517 518 519 520 521
## 41305.08 43945.63 36784.35 32062.80 55957.14 64119.42 67773.07 61150.87
## 522 523 524 525 526 527 528 529
## 62490.61 76078.89 83056.94 58002.33 52871.39 49565.09 53944.91 53451.15
## 530 531 532 533 534 535 536 537
## 43632.85 48625.62 61288.53 55808.37 59206.65 63195.18 60247.22 55277.04
## 538 539 540 541 542 543 544 545
## 48738.08 47393.72 55332.33 55082.70 47641.35 49865.39 49692.02 50385.99
## 546 547 548 549 550 551 552 553
## 41033.24 32867.47 37210.09 45328.18 45125.11 46831.31 40840.03 49854.66
## 554 555 556 557 558 559 560 561
## 51010.18 40787.48 50330.19 58194.42 57559.24 61157.61 56924.07 68686.71
## 562 563 564 565 566 567 568 569
## 85378.34 75467.55 64029.06 68449.06 66573.12 67759.97 59328.47 43314.70
## 570 571 572 573 574 575 576 577
## 50324.58 56230.73 57413.71 59489.60 60136.76 57262.69 69511.47 58883.01
## 578 579 580 581 582 583 584 585
## 52604.91 60207.78 61712.02 54804.87 61072.48 56647.86 53691.91 67264.34
## 586 587 588 589 590 591 592 593
## 52690.41 60035.91 59128.14 52849.30 52154.34 52430.42 43144.70 45942.01
## 594 595 596 597 598 599 600 601
## 40567.56 44814.08 53578.83 46908.94 52768.92 55147.11 60817.11 56974.62
## 602 603 604 605 606 607 608 609
## 61789.16 53356.41 55236.72 56013.18 58321.73 58895.87 58436.55 52613.03
## 610 611 612 613 614 615 616 617
## 59675.82 57735.60 54832.33 51537.89 44499.67 56011.81 59899.86 50833.81
## 618 619 620 621 622 623 624 625
## 61237.49 65423.09 58911.84 81142.93 66263.33 58591.45 60594.70 55947.28
## 626 627 628 629 630 631 632 633
## 46281.13 56990.22 37543.47 37403.68 46973.47 57457.77 55502.07 84235.55
## 634 635 636 637 638 639 640 641
## 74420.24 76621.32 78258.87 72984.39 65702.55 62336.34 50239.45 48607.52
## 642 643 644 645 646 647 648 649
## 47498.93 45980.96 44365.38 47042.62 51660.70 66633.78 81060.18 78183.11
## 650 651 652 653 654 655 656 657
## 79087.01 84959.34 98405.95 93163.80 63429.58 60881.02 57834.96 58819.73
## 658 659 660 661 662 663 664 665
## 55260.19 45671.54 48056.22 52374.63 51566.72 63130.15 63007.83 63297.40
## 666 667 668 669 670 671 672 673
## 51751.19 53110.90 54129.50 49467.61 43446.50 46481.83 44080.30 47567.58
## 674 675 676 677 678 679 680 681
## 45319.70 38156.30 32813.55 32811.05 35560.44 40293.10 42553.84 40558.08
## 682 683 684 685 686 687 688 689
## 40581.67 41030.49 35372.09 41702.36 41199.74 41499.04 43494.80 54204.02
## 690 691 692 693 694 695 696 697
## 62662.48 70712.80 60008.20 56015.43 52897.35 58052.13 48343.67 42041.86
## 698 699 700 701 702 703 704 705
## 35936.75 32655.41 31135.19 36642.57 34905.92 35578.66 41512.40 70175.73
## 706 707 708 709 710 711 712 713
## 76335.13 93884.60 90203.36 92798.97 107826.10 106666.58 84025.49 84372.84
## 714 715 716 717 718 719 720 721
## 75710.06 72812.40 70788.68 53516.17 48913.94 52406.46 49910.95 39024.75
## 722 723 724 725 726 727 728 729
## 44593.41 40864.24 43109.63 40984.56 31690.73 35641.94 36267.25 29902.03
## 730 731 732 733 734 735 736 737
## 47971.85 50219.73 48253.43 53907.96 46313.71 46588.30 54570.59 41020.63
## 738 739 740 741 742 743 744 745
## 37432.26 45934.40 42708.61 44211.36 46980.34 46093.41 42512.15 49501.68
## 746 747 748 749 750 751 752 753
## 44522.27 65486.62 70807.40 66916.36 58850.93 78631.81 71705.43 70624.55
## 754 755 756 757 758 759 760 761
## 55860.41 104584.95 121657.01 126246.39 107770.68 110199.17 109446.54 107475.62
## 762 763 764 765 766 767 768 769
## 60149.36 45199.13 47108.52 45732.95 43709.36 152289.89 156727.12 181933.50
## 770 771 772 773 774 775 776 777
## 185500.28 179437.48 177434.73 184317.43 81527.10 72115.57 38487.68 41920.91
## 778 779 780 781 782 783 784 785
## 42329.92 46727.96 41126.35 41446.37 41288.98 45842.91 40579.79 40277.25
## 786 787 788 789 790 791 792 793
## 45391.59 48416.56 46670.92 44020.02 46758.92 50126.75 50532.64 45075.94
## 794 795 796 797 798 799 800 801
## 41111.62 41696.62 42106.76 36738.92 36820.67 36093.26 35983.93 47586.93
## 802 803 804 805 806 807 808 809
## 50316.34 56929.31 59077.97 53641.86 60804.51 68538.93 57408.59 50482.22
## 810 811 812 813 814 815 816 817
## 44334.05 48144.34 52512.80 50683.41 38691.87 36996.29 44574.69 52925.68
## 818 819 820 821 822 823 824 825
## 44576.19 38959.60 32665.27 43843.28 44021.77 41426.65 35598.25 44764.65
## 826 827 828 829 830 831 832 833
## 52809.86 57273.42 54116.65 53446.40 56009.31 59801.14 60455.03 53758.68
## 834 835 836 837 838 839 840 841
## 55580.08 54998.70 57244.09 60418.21 58581.34 49813.22 55266.30 50825.08
## 842 843 844 845 846 847 848 849
## 44570.94 48367.01 37647.94 37214.46 49458.25 51143.60 52025.78 51663.82
## 850 851 852 853 854 855 856 857
## 55868.15 66683.08 61826.48 50699.77 50024.03 55154.10 61569.49 55481.73
## 858 859 860 861 862 863 864 865
## 50308.73 52119.76 50347.42 52962.50 51058.85 49810.60 64710.41 61315.12
## 866 867
## 68704.05 80711.07
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8088
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 5.421628 0.7664183 3.986956
## t2* 2754.842631 160.1366960 883.779027
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.270346 5.430432 13.74066
## 2 lag_depvar 1691.693925 2791.754064 4551.28425Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# Apply MSTL decomposition
mstl_data_autplt <- forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03")))
# Convert the decomposed time series to a data frame
mstl_df <- data.frame(
Date = as.Date(Gastos_casa$fecha, format="%d/%m/%Y"),
Data = as.numeric(mstl_data_autplt[, "Data"]),
Trend = as.numeric(mstl_data_autplt[, "Trend"]),
Remainder = as.numeric(mstl_data_autplt[, "Remainder"])
)
# Reshape the data frame for ggplot2
mstl_long <- mstl_df %>%
pivot_longer(cols = -Date, names_to = "Component", values_to = "Value")
# Plotting with ggplot2
ggplot(mstl_long, aes(x = Date, y = Value)) +
geom_line() +
theme_bw() +
labs(title = "Descomposición MSTL", x = "Fecha", y = "Valor") +
scale_x_date(date_breaks = "3 months", date_labels = "%m-%Y") +
facet_wrap(~ Component, scales = "free_y", ncol = 1) +
theme(strip.text = element_text(size = 12),
axis.text.x = element_text(angle = 90, hjust = 1))
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"Donaciones/regalos",
gasto=="Regalo chocolates"~"Donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/mantención casa",
gasto=="Chromecast"~"Electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/mantención casa",
gasto=="Sopapo"~"Electrodomésticos/mantención casa",
gasto=="filtro agua"~"Electrodomésticos/mantención casa",
gasto=="ropa tami"~"Donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"Donaciones/regalos",
gasto=="Matri Andrés Kogan"~"Donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"Electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/mantención casa",
gasto=="Reloj"~"Electrodomésticos/mantención casa",
gasto=="Arreglo"~"Electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"Donaciones/regalos",
#2024
gasto=="cartero"~"Correo",
gasto=="correo"~"Correo",
gasto=="Gaviscón y Paracetamol"~"Farmacia",
gasto=="Regalo Matri Cony"~"Donaciones/regalos",
gasto=="Regalo Matri Chepa"~"Donaciones/regalos",
gasto=="Aporte Basureros"~"Donaciones/regalos",
gasto=="donación"~"Donaciones/regalos",
gasto=="Plata Reciclaje y Basurero"~"Donaciones/regalos",
gasto=="basureros"~"Donaciones/regalos",
gasto=="Microondas regalo"~"Donaciones/regalos",
gasto=="Cruz Verde"~"Farmacia",
gasto=="Remedios Covid"~"Farmacia",
gasto=="nacho"~"Electrodomésticos/mantención casa",
gasto=="Jardinero"~"Electrodomésticos/mantención casa",
gasto=="mantencion toyotomi"~"Electrodomésticos/mantención casa",
gasto=="Cámaras Seguridad M.Barrios"~"Electrodomésticos/mantención casa",
gasto=="Uber cumple papá"~"Transporte",
gasto=="Uber"~"Transporte",
gasto=="Uber Matri Cony"~"Transporte",
gasto=="Bencina + tag"~"Gas/Bencina",
gasto=="Bencina + Tag cumple Delox"~"Gas/Bencina",
gasto=="Bencina + peajes Maite"~"Gas/Bencina",
gasto=="Crunchyroll"~"Netflix",
gasto=="Crunchyroll"~"Netflix",
gasto=="Incoludido"~"Enceres",
gasto=="Cortina baño"~"Electrodomésticos/mantención casa",
gasto=="Forro cortina ducha"~"Electrodomésticos/mantención casa",
gasto=="Brussels"~"Comida",
gasto=="Tres toques"~"Enceres",
gasto=="Transferencia"~"Otros",
gasto=="prestamo"~"Otros",
gasto=="Préstamo Andrés"~"Otros",
gasto=="mouse"~"Otros",
gasto=="lamina"~"Otros",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_25<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2025",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_24<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2024",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2020",fecha_month)) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_25 %>%
dplyr::right_join(fit_month_gasto_24,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2025","2024","2023","2022","2021","2020"))| Item | 2025 | 2024 | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|---|---|
| Agua | 6.382333 | 6.993667 | 5.195333 | 5.410333 | 5.849167 | 9.93775 |
| Comida | 234.753000 | 326.890000 | 366.009167 | 312.386750 | 317.896583 | 392.93367 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electricidad | 49.655556 | 83.582750 | 38.104750 | 47.072333 | 29.523000 | 20.60458 |
| Enceres | 2.964444 | 23.989000 | 18.259750 | 24.219750 | 14.801167 | 39.01200 |
| Farmacia | 0.000000 | 0.000000 | 10.704083 | 2.835000 | 13.996083 | 14.03675 |
| Gas/Bencina | 38.538889 | 44.292667 | 42.636000 | 45.575000 | 13.583667 | 17.25833 |
| Diosi | 20.275444 | 33.319583 | 55.804250 | 31.180667 | 52.687833 | 37.12133 |
| donaciones/regalos | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| Electrodomésticos/ Mantención casa | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 |
| VTR | 17.108889 | 18.326667 | 12.829167 | 25.156667 | 19.086917 | 19.11375 |
| Netflix | 0.000000 | 1.391417 | 8.713833 | 7.151583 | 7.028750 | 8.24725 |
| Otros | 0.000000 | 76.164000 | 5.481667 | 5.000000 | 0.000000 | 0.00000 |
| Total | 369.678556 | 614.949750 | 563.738000 | 505.988083 | 474.453167 | 558.26542 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")
uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table")
tryCatch(uf25 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2025.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf25 <-uf25[[length(uf25)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf25 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf23[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2025, uf25)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 54 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2838, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)## Warning in bats(as.numeric(y), use.box.cox = use.box.cox, use.trend =
## use.trend, : optim() did not converge.fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)# Configurar API Key
nixtlar::nixtla_set_api_key(Sys.getenv("API_NIXTLA"))## API key has been set for the current session.try(nixtlar::nixtla_set_api_key(Sys.getenv("NIXTLA")))## API key has been set for the current session.# Preparar datos en formato requerido por TimeGPT
uf_timegpt <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y)
# Realizar pronóstico con TimeGPT
timegpt_fcst <- nixtlar::nixtla_client_forecast(
uf_timegpt,
h = 298, # 298 días a pronosticar
freq = "D", # Frecuencia diaria
add_history = TRUE, # Incluir datos históricos en el output
level = c(80,95),
model= "timegpt-1-long-horizon",
clean_ex_first = TRUE
)## The specified horizon h exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.# 1. Convertir 'ds' a fecha en ambas tablas
uf_timegpt <- uf_timegpt %>%
mutate(ds = as.Date(ds))
timegpt_fcst <- timegpt_fcst %>%
mutate(ds = as.Date(ds))
# 2. Combinar los datos históricos y el pronóstico
full_data <- bind_rows(
uf_timegpt %>% mutate(type = "Histórico"),
timegpt_fcst %>% mutate(type = "Pronóstico")
)
# Visualizar resultados
ggplot(full_data, aes(x = ds, y = TimeGPT)) +
# Intervalo de confianza del 95%
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
# Intervalo de confianza del 80%
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
# Línea histórica
geom_line(data = filter(full_data, type == "Histórico"),
aes(color = "Histórico"), size = 1) +
# Línea de pronóstico
geom_line(data = filter(full_data, type == "Pronóstico"),
aes(color = "Pronóstico"), size = 1) +
# Línea vertical separadora
geom_vline(xintercept = max(filter(full_data, type == "Histórico")$ds),
linetype = "dashed", color = "red", size = 0.8) +
# Configuración del eje x
scale_x_date(
date_breaks = "3 months", # Reduce la frecuencia de las etiquetas
date_labels = "%b %Y", # Formato de etiquetas (mes y año)
) +
# Configuración del eje y
scale_y_continuous(labels = function(x) format(x, scientific = FALSE)) +
# Configuración de colores
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
# Títulos y subtítulos
labs(
title = "Pronóstico de Serie Temporal con TimeGPT",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Valor",
color = "Leyenda"
) +
# Tema y estilos
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1, size = 8),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)## Warning: Removed 2838 rows containing missing values or values outside the scale range
## (`geom_ribbon()`).
## Removed 2838 rows containing missing values or values outside the scale range
## (`geom_ribbon()`).## Warning: Removed 2838 rows containing missing values or values outside the scale range
## (`geom_line()`).
library(prophet)## Warning: package 'prophet' was built under R version 4.4.3## Loading required package: Rcpp## Warning: package 'Rcpp' was built under R version 4.4.3## Loading required package: rlang## Warning: package 'rlang' was built under R version 4.4.3##
## Attaching package: 'rlang'## The following objects are masked from 'package:purrr':
##
## %@%, flatten, flatten_chr, flatten_dbl, flatten_int, flatten_lgl,
## flatten_raw, invoke, splice## The following object is masked from 'package:sparklyr':
##
## invoke## The following object is masked from 'package:data.table':
##
## := model <- prophet(
cbind.data.frame(ds= as.Date(uf_timegpt$ds), y=uf_timegpt$y),
# Trend flexibility
growth = "linear",
changepoint.prior.scale = 0.05, # Reduced for smoother trend
n.changepoints = 50, # Increased from default 25
# Seasonality
yearly.seasonality = TRUE,
weekly.seasonality = TRUE,
daily.seasonality = FALSE, # Disabled for daily data
seasonality.mode = "additive",
seasonality.prior.scale = 15, # Increased to capture stronger seasonality
# Holidays (if applicable)
# holidays = generated_holidays # Create with add_country_holidays()
# Uncertainty intervals
interval.width = 0.95,
uncertainty.samples = 1000
)
future <- make_future_dataframe(model, periods = 298, include_history = T)
forecast <- predict(model, future)
forecast <- forecast[, c("ds", "yhat", "yhat_lower", "yhat_upper")]
forecast$pred <- ifelse(forecast$ds > max(uf_timegpt$ds), 1,0)## Warning in check_tzones(e1, e2): 'tzone' attributes are inconsistentforecast$ds <- as.Date(forecast$ds)
ggplot(forecast, aes(x = ds, y = yhat)) +
geom_ribbon(aes(ymin = yhat_lower, ymax = yhat_upper),
fill = "#9ecae1", alpha = 0.4) +
geom_line(color = "#08519c", linewidth = 0.8) +
geom_vline(xintercept = max(uf_timegpt$ds), color = "red", linetype = "dashed", linewidth=1) +
scale_x_date(date_breaks = "6 months", date_labels = "%y %b") +
scale_y_continuous(labels = scales::comma) +
labs(title = "Valores predichos (95%IC)",
# subtitle = "March 10, 2025 - May 7, 2025",
x = "Fecha",
y = "Valor",
# caption = "Source: Prophet Forecast Model"
) +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
plot.subtitle = element_text(color = "gray50"),
axis.text.x = element_text(angle = 45, hjust = 1),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
plot.caption = element_text(color = "gray30")
)
La proyección de la UF a 298 días más 2025-10-09 00:04:58 sería de: 26.544 pesos// Percentil 95% más alto proyectado: 35.016,65
Según TimeGPT: La proyección de la UF a 298 días más 2026-08-03 sería de: 39.999,44 pesos// Percentil 80% más alto proyectado: 42.030,93 pesos// Percentil 95% más alto proyectado: 42.301,02
Según prophet: La proyección de la UF a 298 días más 2026-08-03 sería de: 42.500 pesos// Percentil 95% más alto proyectado: 49.080
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## i Please use tidy evaluation idioms with `aes()`.
## i See also `vignette("ggplot2-in-packages")` for more information.
## i The deprecated feature was likely used in the ggfortify package.
## Please report the issue at <https://github.com/sinhrks/ggfortify/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 26163.23 | 26324.26 |
| Lo.80 | 26294.43 | 26488.59 |
| Point.Forecast | 26544.08 | 26799.01 |
| Hi.80 | 31372.77 | 32157.70 |
| Hi.95 | 34274.97 | 34994.42 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.4461 1030.7989
## s.e. 0.1017 38.8318
##
## sigma^2 = 38578: log likelihood = -535.03
## AIC=1076.06 AICc=1076.38 BIC=1083.21paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.4530 591.1895 13.4662
## s.e. 0.1031 309.9789 9.4105
##
## sigma^2 = 38045: log likelihood = -533.96
## AIC=1075.93 AICc=1076.46 BIC=1085.45forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 624.0144 | 600.6350 | 590.2443 |
| Lo.80 | 772.4413 | 749.5237 | 680.5277 |
| Point.Forecast | 1052.8264 | 1030.7811 | 890.4046 |
| Hi.80 | 1333.2114 | 1312.0385 | 1213.2736 |
| Hi.95 | 1481.6383 | 1460.9272 | 1429.1281 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
theme_custom() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Gastos_casa_mensual_2022$mes_ano <- gsub("marzo", "Mar", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("abril", "Apr", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("mayo", "May", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("junio", "Jun", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("julio", "Jul", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("agosto", "Aug", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("septiembre", "Sep", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("octubre", "Oct", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("noviembre", "Nov", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("diciembre", "Dec", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("enero", "Jan", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022$mes_ano <- gsub("febrero", "Feb", Gastos_casa_mensual_2022$mes_ano)
Gastos_casa_mensual_2022<- dplyr::filter(Gastos_casa_mensual_2022, !is.na(Tami))
Gastos_casa_mensual_2022$mes_ano <- parse_date_time(Gastos_casa_mensual_2022$mes_ano, "%b_%Y")
Gastos_casa_mensual_2022$mes_ano <- as.Date(as.character(Gastos_casa_mensual_2022$mes_ano))
Gastos_casa_mensual_2022_timegpt <- Gastos_casa_mensual_2022 %>%
mutate(value = Tami + Andrés) %>%
rename(ds = mes_ano, y = value) %>%
mutate(#ds= format(ds, "%Y-%m"),
unique_id = "1") %>% #it is only one series
select(unique_id, ds, y)
#Convertir la base de UF a mensual
uf_timegpt_my <- uf_serie_corrected %>%
dplyr::rename(ds = date3, y = value) %>%
dplyr::mutate(ds = format(ds, "%Y-%m-%d")) %>%
dplyr::mutate(unique_id = "serie_1")%>%
dplyr::select(unique_id, ds, y) %>%
mutate(ds = ymd(ds)) %>% # Convert 'ds' to Date
mutate(month = month(ds), year = year(ds)) %>% # Extract month and year
group_by(month, year) %>% # Group by month and year
summarise(average_y = mean(y))%>% # Calculate average y
mutate(ds = as.Date(paste0(year,"-",month, "-01")))%>%
ungroup()%>%
select(ds, uf=average_y)
Gastos_casa_mensual_2022_timegpt_ex<-
Gastos_casa_mensual_2022_timegpt |>
dplyr::left_join(uf_timegpt_my, by=c("ds"="ds"))
#Historical Exogenous Variables: These should be included in the input data immediately following the id_col, ds, and y columns
gastos_timegpt_fcst <- nixtlar::nixtla_client_forecast(
Gastos_casa_mensual_2022_timegpt_ex,
h = 12,
freq = "M", # Monthly frequency
add_history = TRUE,
level = c(80, 95),
model = "timegpt-1",#"timegpt-1-long-horizon",
clean_ex_first = TRUE
)
# Convert 'ds' to Date format in both tables
Gastos_casa_mensual_2022_timegpt_corr <- Gastos_casa_mensual_2022_timegpt %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
gastos_timegpt_fcst <- gastos_timegpt_fcst %>%
mutate(ds = as.Date(paste0(ds, "-01"))) # Add day to make it a complete date
# Combine historical and forecast data
full_data_gastos <- bind_rows(
Gastos_casa_mensual_2022_timegpt_corr %>% mutate(type = "Histórico"),
gastos_timegpt_fcst %>% mutate(type = "Pronóstico")
)
full_data_gastos |>
dplyr::mutate(y= ifelse(is.na(y),TimeGPT, y)) |>
# Visualize results
ggplot(aes(x = ds, y = y)) +
geom_ribbon(aes(ymin = `TimeGPT-lo-95`, ymax = `TimeGPT-hi-95`),
fill = "#4B9CD3", alpha = 0.2) +
geom_ribbon(aes(ymin = `TimeGPT-lo-80`, ymax = `TimeGPT-hi-80`),
fill = "#4B9CD3", alpha = 0.3) +
geom_line(aes(color = type), linewidth = 1.5) +
geom_vline(xintercept = max(filter(full_data_gastos, type == "Histórico")$ds),
linetype = "dashed", color = "red", linewidth = 0.8) +
scale_x_date(
date_breaks = "3 months",
date_labels = "%b %Y"
) +
scale_y_continuous(
name = "Gastos Totales",
labels = scales::comma,
breaks = pretty(full_data_gastos$y, n = 10),
expand = expansion(mult = c(0.05, 0.05))
) +
scale_color_manual(
name = "Leyenda",
values = c("Histórico" = "black", "Pronóstico" = "#4B9CD3")
) +
labs(
title = "Pronóstico de Gastos Mensuales (TimeGPT, ajustando por UF promedio mensual)",
subtitle = "Intervalos de confianza al 80% (más oscuro) y 95% (más claro)",
x = "Fecha",
y = "Gastos Totales",
color = "Leyenda"
) +
theme_minimal() +
theme(
axis.text.x = element_text(angle = 45, hjust = 1),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.position = "bottom",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank()
)
Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.4.0 (2024-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 26100)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
## system code page: 65001
##
## time zone: UTC
## tzcode source: internal
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] prophet_1.0 rlang_1.1.6 Rcpp_1.1.0 scales_1.4.0
## [5] ggiraph_0.9.1 tidytext_0.4.3 DT_0.34.0 autoplotly_0.1.4
## [9] rvest_1.0.5 plotly_4.11.0 xts_0.14.1 forecast_8.24.0
## [13] wordcloud_2.6 RColorBrewer_1.1-3 SnowballC_0.7.1 tm_0.7-16
## [17] NLP_0.3-2 tsibble_1.1.6 lubridate_1.9.4 forcats_1.0.1
## [21] dplyr_1.1.4 purrr_1.1.0 tidyr_1.3.1 tibble_3.3.0
## [25] ggplot2_4.0.0 tidyverse_2.0.0 sjPlot_2.9.0 lattice_0.22-6
## [29] gridExtra_2.3 plotrix_3.8-4 sparklyr_1.9.2 httr_1.4.7
## [33] readxl_1.4.5 zoo_1.8-14 stringr_1.5.2 stringi_1.8.7
## [37] DataExplorer_0.8.4 data.table_1.17.8 reshape2_1.4.4 fUnitRoots_4040.81
## [41] plyr_1.8.9 readr_2.1.5
##
## loaded via a namespace (and not attached):
## [1] rstudioapi_0.17.1 jsonlite_2.0.0 magrittr_2.0.4
## [4] farver_2.1.2 rmarkdown_2.30 vctrs_0.6.5
## [7] askpass_1.2.1 janitor_2.2.1 htmltools_0.5.8.1
## [10] curl_7.0.0 janeaustenr_1.0.0 cellranger_1.1.0
## [13] Formula_1.2-5 TTR_0.24.4 StanHeaders_2.32.10
## [16] parallelly_1.45.1 sass_0.4.10 KernSmooth_2.23-22
## [19] bslib_0.9.0 htmlwidgets_1.6.4 tokenizers_0.3.0
## [22] extraDistr_1.10.0 httr2_1.2.1 cachem_1.1.0
## [25] uuid_1.2-1 networkD3_0.4.1 igraph_2.1.4
## [28] lifecycle_1.0.4 pkgconfig_2.0.3 Matrix_1.7-0
## [31] R6_2.6.1 fastmap_1.2.0 future_1.67.0
## [34] snakecase_0.11.1 digest_0.6.37 selectr_0.4-2
## [37] colorspace_2.1-2 spatial_7.3-17 crosstalk_1.2.2
## [40] labeling_0.4.3 timechange_0.3.0 abind_1.4-8
## [43] compiler_4.4.0 bit64_4.6.0-1 withr_3.0.2
## [46] inline_0.3.21 S7_0.2.0 tseries_0.10-58
## [49] carData_3.0-5 DBI_1.2.3 QuickJSR_1.8.1
## [52] pkgbuild_1.4.8 gplots_3.2.0 openssl_2.3.4
## [55] rappdirs_0.3.3 loo_2.8.0 fBasics_4041.97
## [58] gtools_3.9.5 caTools_1.18.3 tools_4.4.0
## [61] lmtest_0.9-40 quantmod_0.4.28 future.apply_1.20.0
## [64] nnet_7.3-19 glue_1.8.0 quadprog_1.5-8
## [67] nlme_3.1-164 nixtlar_0.6.2 generics_0.1.4
## [70] gtable_0.3.6 tzdb_0.5.0 hms_1.1.3
## [73] xml2_1.4.0 car_3.1-3 pillar_1.11.1
## [76] vroom_1.6.6 bit_4.6.0 tidyselect_1.2.1
## [79] its.analysis_1.6.0 knitr_1.50 urca_1.3-4
## [82] stats4_4.4.0 xfun_0.53 matrixStats_1.5.0
## [85] timeDate_4041.110 rstan_2.32.7 lazyeval_0.2.2
## [88] yaml_2.3.10 boot_1.3-30 evaluate_1.0.5
## [91] codetools_0.2-20 timeSeries_4041.111 data.tree_1.2.0
## [94] cli_3.6.5 RcppParallel_5.1.11-1 systemfonts_1.3.1
## [97] jquerylib_0.1.4 globals_0.18.0 dbplyr_2.5.1
## [100] anytime_0.3.12 parallel_4.4.0 ggfortify_0.4.19
## [103] ellipsis_0.3.2 fracdiff_1.5-3 bitops_1.0-9
## [106] listenv_0.9.1 viridisLite_0.4.2 slam_0.1-55
## [109] crayon_1.5.3#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))